Recon2Reason-Reasoning

Recon2Reason Reasoning 4B is a 4B-scale vision-language model specialized for spatial reasoning in indoor and embodied scenes. It is fine-tuned from Qwen3-VL-4B and improves reasoning about metric distance, relative position, object configuration, and spatial relations from visual inputs.

The checkpoint retains the standard Qwen3VLForConditionalGeneration architecture. No custom model code or trust_remote_code=True is required.

Highlights

• Standard Qwen3-VL Transformers interface
• 4,437,815,808 parameters
• BF16 weights in sharded Safetensors format
• Single-image and multi-image visual inputs
• Strong results on metric and qualitative spatial reasoning benchmarks

This repository contains the reasoning model only. The retrieval-augmented scene-reconstruction extension is released separately.

Model details

Quick start

pip install "transformers==4.57.1" "torch>=2.6" accelerate safetensors pillow

import torch
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration

model_id = "BAAI/Recon2Reason-Reasoning-4B"

processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3VLForConditionalGeneration.from_pretrained(
    model_id,
    dtype="auto",
    device_map="auto",
).eval()

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "path/to/scene.jpg"},
            {
                "type": "text",
                "text": "Which object is closest to the chair? ",
            },
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

with torch.inference_mode():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=512,
        do_sample=False,
    )

generated_ids = output_ids[:, inputs["input_ids"].shape[1]:]
answer = processor.batch_decode(
    generated_ids,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False,
)[0]
print(answer)

The model uses the standard Qwen3-VL chat format. For benchmark reproduction, use greedy decoding (do_sample=False) unless a benchmark specifies otherwise.

Evaluation

The checkpoint was evaluated in BF16 with PyTorch 2.8.0, Transformers 4.57.1, SDPA attention, and greedy decoding on NVIDIA RTX PRO 6000 Blackwell GPUs.

License

The model is released under the Apache License 2.0, subject to final confirmation that all training data and upstream artifacts permit this distribution. See LICENSE.

Acknowledgements

This work builds on Qwen3-VL. We thank the creators of the evaluation datasets and the open-source Transformers ecosystem.